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    SYSTEM AND METHOD FOR ADDRESSING OVERFITTING IN A NEURAL NETWORK
    1.
    发明申请
    SYSTEM AND METHOD FOR ADDRESSING OVERFITTING IN A NEURAL NETWORK 审中-公开
    用于解决神经网络覆盖的系统和方法

    公开(公告)号：US20160335540A1

    公开(公告)日：2016-11-17

    申请号：US15222870

    申请日：2016-07-28

    Applicant: Google Inc.

    Inventor： Geoffrey E. Hinton ,  Alexander Krizhevsky ,  Ilya Sutskever ,  Nitish Srivastva

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/084 ,  G06K9/4628 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/082

    Abstract: A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data.

    Abstract translation: 用于训练神经网络的系统。 开关被连接到神经网络的至少一些层中的特征检测器。 对于每个训练情况，交换机根据预配置的概率随机选择性地禁用每个特征检测器。 然后对每个训练情况的权重进行归一化，以将神经网络应用于测试数据。

    System and method for addressing overfitting in a neural network
    3.
    发明授权
    System and method for addressing overfitting in a neural network 有权
    用于解决神经网络过拟合的系统和方法

    公开(公告)号：US09406017B2

    公开(公告)日：2016-08-02

    申请号：US14015768

    申请日：2013-08-30

    Applicant: Google Inc.

    Inventor： Geoffrey E. Hinton ,  Alexander Krizhevsky ,  Ilya Sutskever ,  Nitish Srivastva

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/084 ,  G06K9/4628 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/082

    Abstract: A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data.

    Abstract translation: 用于训练神经网络的系统。 开关被连接到神经网络的至少一些层中的特征检测器。 对于每个训练情况，交换机根据预配置的概率随机选择性地禁用每个特征检测器。 然后对每个训练情况的权重进行归一化，以将神经网络应用于测试数据。

    TRAINING DISTILLED MACHINE LEARNING MODELS
    8.
    发明申请
    TRAINING DISTILLED MACHINE LEARNING MODELS 审中-公开
    培训机器学习模式

    公开(公告)号：US20150356461A1

    公开(公告)日：2015-12-10

    申请号：US14731349

    申请日：2015-06-04

    Applicant: Google Inc.

    Inventor： Oriol Vinyals ,  Jeffrey A. Dean ,  Geoffrey E. Hinton

    IPC: G06N99/00 ,  G06N7/00

    CPC classification number: G06N99/005 ,  G06N3/0454 ,  G06N7/00 ,  G06N7/005

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.

    Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于训练蒸馏机器学习模型。 其中一种方法包括训练繁琐的机器学习模型，其中笨重的机器学习模型被配置为接收输入并为多个类中的每一个生成相应的分数; 并且在多个训练输入上训练蒸馏机器学习模型，其中所述蒸馏机器学习模型还被配置为接收所述多个类别的输入并生成分数，其包括：使用所述麻烦的机器学习模型来处理每个训练输入以产生 训练输入的麻烦目标软输出; 并训练蒸馏机器学习模型，对于每个训练输入，产生与训练输入的麻烦的目标软输出相匹配的软输出。

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